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15th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2019

作者： Wu, Jiawei Yu, Zhaochai Zhang, Zuchang Li, Zuoyong Liu, Weina Yu, Jiale College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Department of Computer Engineering Fujian Polytechnic of Information Technology Fuzhou350003 China

ISBN: (纸本)9789811397097

To alleviate the problems of slow speed and weak correction ability of existing license plate correction methods under complex conditions, this paper presents a faster license plate positioning method based on the color component combination and color region fusion and develops a more accurate correction algorithm of blue license plate using probabilistic Hough transform and perspective transform. The proposed methods utilize the characteristics of white characters on the blue background of the Chinese license plate. Color component combination in HSV and RGB color spaces and image thresholding are first performed to obtain the background region of the blue license plate and its character region. Then, both regions are fused to obtain complete and accurate license plate region. And finally, edge detection, probabilistic Hough transform, and perspective transform are performed to achieve rapid license plate correction. Experimental results show that average correction time of blue license plate obtained by the proposed method is 0.023 s, and the average correction rate is 95.0%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Hough transforms

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Magnetic plasmon resonances in nanostructured topological insulators for strongly enhanced light-MoS_(2) interactions

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Light(Science & Applications) 2020年第1期9卷 155-164页

作者： Hua Lu Zengji Yue Yangwu Li Yinan Zhang Mingwen Zhang Wei Zeng Xuetao Gan Dong Mao Fajun Xiao Ting Mei Weiyao Zhao Xiaolin Wang Min Gu Jianlin Zhao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical University710129 Xi’anChina Institute for Superconducting&Electronic Materials and ARC Centre of Excellence in Future Low-Energy Electronics University of WollongongNorth WollongongNSW 2500Australia Center for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology200093 ShanghaiChina Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics TechnologyJinan University510632 GuangzhouChina State Key Laboratory of Solidification Processing School of Materials Science and EngineeringNorthwestern Polytechnical University710072 Xi’anChina

Magnetic resonances not only play crucial roles in artificial magnetic materials but also offer a promising way for light control and interaction with ***,magnetic resonance effects have attracted special attention in plasmonic systems for overcoming magnetic response saturation at high frequencies and realizing high-performance optical *** novel states of matter,topological insulators(TIs)present topologically protected conducting surfaces and insulating bulks in a broad optical range,providing new building blocks for ***,until now,high-frequency(*** range)magnetic resonances and related applications have not been demonstrated in TI ***,we report for the first time,to our knowledge,a kind of visible range magnetic plasmon resonances(MPRs)in TI structures composed of nanofabricated Sb_(2)Te_(3) *** experimental results show that the MPR response can be tailored by adjusting the nanogroove height,width,and pitch,which agrees well with the simulations and theoretical ***,we innovatively integrated monolayer MoS_(2) onto a TI nanostructure and observed strongly reinforced light-MoS_(2) interactions induced by a significant MPR-induced electric field enhancement,remarkable compared with TI-based electric plasmon resonances(EPRs).The MoS_(2) photoluminescence can be flexibly tuned by controlling the incident light *** results enrich TI optical physics and applications in highly efficient optical functionalities as well as artificial magnetic materials at high frequencies.

关键词： topological resonance visible

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Identifying key Learner on Online E-Learning Platform: An Effective Resistance Distance Approach 15th

Identifying Key Learner on Online E-Learning Platform: An Ef...

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作者： Lu, Chunhua Zhang, Fuquan Li, Yunpeng School of Electronic and Information Engineering Anshun University Guizhou561000 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Computer School Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9789811397097

Teachers are never the only teacher in the class, especially in online e-learning environment. The key learner who is supposed to be more active and eager to spread knowledge and motivation to other classmates has a huge potentiality to improve the quality of teaching. However, the identification of such key learner is challenging which needs lots of human experience, especially when the contact channels between teachers and students are much more monotonous in online e-learning environment. Inspired by resistance distance theory, in this paper, we apply resistance distance and centrality into an interactive network of learners to identify key learner who can effectively motivate the whole class with discussion in e-learning platform. First, we define the terms of interactive network of learners with the node, edge, and graph. Then the distance between nodes is replaced with effective resistance distance to gain better understanding of propagation among the learners. Afterward, Closeness Centrality is utilized to measure the centrality of each learner in interactive network of learners. Experimental results show that the centrality we use can cover and depict the learners’ discussion activities well, and the key learner identified by our approach under apposite stimuli can effectively motivate the whole class’ learning performance. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Graphic methods

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Deep Learning-based Multi-task Network for Intelligent Management of Garbage Deposit Points

Deep Learning-based Multi-task Network for Intelligent Manag...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Yezhen Wang Haobin Zheng Changjiang Mao Jing Zhang Xiao Ke College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou

ISBN: (纸本)9781728194202

With the economic and social development and the substantial improvement of material conditions, the generation of domestic waste has grown rapidly and has become a constraint factor for the development of new urbanization. In the past few years, research on the domestic waste industry has been limited to intelligent waste sorting, neglecting the role of intelligent management of waste storage sites. To relieve it, We propose a deep learning-based multi-task network for intelligent management of garbage deposit points, which combines algorithms such as YoloV5,Deepsort, Insightface, and Openpose to achieve waste bin detection, waste bin status recognition and analysis, face recognition, action recognition, and multiple object tracking based on real-time surveillance video. Besides, we propose a new dataset named Waste Bin Status, which provides a meaningful addition to the existing field of waste bin identification. Experiments on WBS dataset validate that our method is superior to other methods for garbage point status identification. Moreover, our network is trained to work with different scenarios of garbage deposits, demonstrating state-of-the-art performance in real-world tests.

关键词： Industries Face recognition Surveillance Education Streaming media Multitasking Real-time systems

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Online Tensor Decomposition for Hyperspectral Data Representation

Online Tensor Decomposition for Hyperspectral Data Represent...

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2021 China Automation Congress, CAC 2021

作者： He, Jinrong Shi, Yanxin Xie, Chengwang Bao, Zeyu Bai, Zongwen College of Mathematics and Computer Science Yan'an University Shaanxi China School of Computer and Information Engineering South China Normal University Shanwei China School of the Gifted Young University of Science and Technology of China Hefei China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Shaanxi China

ISBN: (纸本)9781665426473

Tensor is a natural representation of visual data, such as hyperspectral image data. Other than vector representation based feature extraction methods, tensor-based representation methods are more informative. Most existing data representation methods only work on vector spaces which are implemented by stacking the original tensor elements into a long vector. However, the vectorization will inevitably lost the underlying structural information and lead to high computational complexity. In order to reduce the memory requirements, we proposed an online tensor decomposition (OTD) method for hyperspectral image feature extraction. It does not require to load the whole tensor data set into main memory, and with the insertion of new tensor samples, it can update projection matrices incrementally, which is desirable for large data sets. In the experiments, the proposed method was applied into the task of apple hyperspectral data classification. The classification results confirm the effectiveness of online tensor decom-position method. © 2021 IEEE

关键词： Principal component analysis

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A Community Detection Approach for Academic Social Network: the Case of the Cross-Strait Academic Conference Network

A Community Detection Approach for Academic Social Network: ...

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International Conference on Electronics, Communications and information technology (CECIT)

作者： Xinxin Zhang Li Xu Min Gao Jianxi Fan Hung-Min Sun Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China The School of Computer Science Fudan University Shanghai China The School of Computer Science and Technology Soochow University Suzhou China The Department of Computer Science National Tsing Hua University Hsinchu Taiwan

With the fast development of information networks, academic social network analysis has attracted significant attention, we regard these researchers sharing common interests as a research community. The community has been widely used as the underlying structure of social networks. To utilize network information as much as possible, we capture the structural characteristics and attribute features to detect communities in the cross-strait academic conference network. In this paper, we first consider 2-hop neighbors with the Pareto principle to filtrate the central seed nodes in networks. Then we propose a new seed shrinkage strategy to get the initial communities and define the similarity according to the structure and attributes of social networks to merge the communities. Finally, we define a new indicator named inner average distance (IAD) to measure the aggregation density for each community. A case study of the cross-strait academic social network was developed to test the applicability of the proposed model, the experiment analysis suggests that our method can be more accurate to identify communities in most situations, which contributes to controlling the information spreading in networks.

关键词： Analytical models Social networking (online) Density measurement Feature extraction information and communication technology Indexes

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Optimal Design of Online Peer Assessment System 15th

Optimal Design of Online Peer Assessment System

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作者： Lin, Yeyu Lin, Yaming Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Computer and Control Engineering Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811397097

A feasible way to do a formative evaluation is to use peer assessment. In other words, students play the role of evaluators to evaluate the work submitted by others. However, the reliability of students’ rating is not guaranteed. Therefore, we propose a new strategy to design and develop an online peer assessment system to support the effective development of blended learning activities for engineering courses under the new situation. Empirical research shows that teachers and students are more satisfied with the system. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Students

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation

Attention Residual Network for White Blood Cell Classificati...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Meng Zhao Lingmin Jin Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781728194202

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods.

关键词： Training White blood cells Costs Convolution Training data Feature extraction Generators

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Transfer learning-based few-shot sample deep learning-assisted template attacks

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Physical Communication 2025年 72卷

作者： Feng, Zhiying Li, Lang Ou, Yu Deng, Lianrui College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang421002 China

Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments. © 2025 Elsevier B.V.

关键词： Deep learning

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DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

arXiv

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arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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